# data[].attributes.body
This is an example of a good graph because all the pieces are proportionate and correspond to their percentage (50% doesn't look like 3%). Also, the graph is color coded well which makes it easy to interpret. Lastly the graph is labeled so we know exactly what the data means.

# data[].attributes.body
This is an example of a good graph because it has a legend at the side which makes for easy interpretation. The graph clearly shows the percentages of each category so we know which area is contributing what amount of revenue. The only thing I would change about this graph are the colors because some of the shades may look similar for those who have eyesight problems.

# data[].attributes.body
This is an example of a bad graph because the percentages do not correlate to anything. This could be a study done on the entire US or just a small community, but the graph does not specify a demographic. Also, the results may be misleading because people may have fit into all three categories so there could be repetition which could cause inaccurate results.

# data[].attributes.body
This graph is misleading because it does not clearly label what is being measured in the x or y axis. There are a lot of points of data, but none explain what they represent. Also, in general there is a lot of data and the information can be overwhelming to look at.